Higgs-mass dependence of the effective electroweak mixing angle <mml:math altimg="si1.gif" overflow="scroll" xmlns:xocs="http://www.elsevier.com/xml/xocs/dtd" xmlns:xs="http://www.w3.org/2001/XMLSchema" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns="http://www.elsevier.com/xml/ja/dtd" xmlns:ja="http://www.elsevier.com/xml/ja/dtd" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:tb="http://www.elsevier.com/xml/common/table/dtd" xmlns:sb="http://www.elsevier.com/xml/common/struct-bib/dtd" xmlns:ce="http://www.elsevier.com/xml/common/dtd" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:cals="http://www.elsevier.com/xml/common/cals/dtd"><mml:msup><mml:mi mathvariant="normal">sin</mml:mi><mml:mn>2</mml:mn></mml:msup><mml:msub><mml:mi>θ</mml:mi><mml:mi mathvariant="normal">eff</mml:mi></mml:msub></mml:math> at the two-loop level

It happens that s2 and ŝ2 are equal with 0.1% accuracy, though they are split by radiative corrections and a natural estimate for their difference is 1%. This degeneracy occurs only for mt value close to 170 GeV, so no deep physical reason can be attributed to it. However, another puzzle of the standard model, the degeneracy of [Formula: see text] and s2, is not independent of the previous one since a good physical reason exists for [Formula: see text] and ŝ2 degeneracy. We present explicit formulas which relate these three angles.

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Higgs mass dependence of electroweak radiative corrections and the triviality bound

Physics Letters B ◽

10.1016/0370-2693(93)90689-f ◽

1993 ◽

Vol 301 (2-3) ◽

pp. 203-207 ◽

Cited By ~ 6

Author(s):

S. Cortese ◽

E. Pallante ◽

R. Petronzio

Keyword(s):

Higgs Mass ◽

Radiative Corrections ◽

Mass Dependence ◽

Electroweak Radiative Corrections

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Using Machine Learning for Precision Measurements

EPJ Web of Conferences ◽

10.1051/epjconf/201921406022 ◽

2019 ◽

Vol 214 ◽

pp. 06022

Author(s):

Dimitri Bourilkov

Keyword(s):

Machine Learning ◽

Signal To Noise Ratio ◽

Hadron Collider ◽

New Physics ◽

Precise Determination ◽

Machine Learning Techniques ◽

Test Case ◽

Mixing Angle ◽

Learning Techniques ◽

Electroweak Mixing Angle

The use of machine learning techniques for classification is well established. They are applied widely to improve the signal-to-noise ratio and the sensitivity of searches for new physics at colliders. In this study I explore the use of machine learning for optimizing the output of high precision experiments by selecting the most sensitive variables to the quantity being measured. The precise determination of the electroweak mixing angle at the Large Hadron Collider using linear or deep neural network regressors is developed as a test case.

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